Current systems for supporting clinical diagnosis rely on an efficient management, linking as well as accessing of heterogeneous knowledge and data resources, such as personal patient records including data ranging from structured to unstructured data and from annotated medical images to lab results to dictated reports.
Although large amounts of clinical data is available, it is still difficult to automatically use and integrate the data within currently used clinical diagnose decision support systems.
This is mainly due to a lack of seamless integration of information and knowledge in current systems for supporting clinical diagnosis. In particular, the integration of knowledge and information requires the availability of semantic annotation of information entities on the respective level of detail in order to explicitly capture their content information as well as the interpretation of annotations, e.g. the significance of a particular observation in the context of likely diseases.
Although annotations are supported by most currently used systems an integration of annotated patient data within clinical decision support systems is still difficult to realize. This is due to the fact, that the corresponding annotations do only capture the descriptive information of its content, i.e. the observations made, the findings discovered, the various symptoms identified.
However, in clinical diagnosis decision systems, the descriptive data items need to be interpreted in the context of one particular or a set of likely diseases. For being able to automatically infer the relevance of symptoms and findings in the context of a particular disease, explicit information about relations between possible symptoms and possible diagnoses would be required.
Clinicians are usually experts in one particular domain, such that they often lack prior knowledge of how particular symptoms might relate to diseases that are out-of-scope of their expertise. In other words, there is the clear danger that the information about the relevance of identified symptoms remains overlooked or misinterpreted, leading to wrong or not appropriate treatments, etc.